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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Distinguishing the role from ML engineers and solutions engineers
- Focusing on outcomes rather than delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprises
- Observing workflows from start to finish
- Mapping systems, data flows, and handoff points
- Identifying the specific constraints that hinder value creation
Problem Scoping and Prioritisation
- Qualifying use cases for feasibility
- Scoring candidate workflows against effort required
- Selecting the initial workflow to validate
Value Framing and Acceptance Criteria
- Establishing metric baselines before development begins
- Using business-case language suitable for finance sponsors
- Aligning acceptance criteria with end users
Model Selection: Frontier and Open Weights
- Comparing GPT-6, Claude 5.5, and open-source models
- Trade-offs between context window, latency, and unit cost
- Situations where data regulations necessitate locally hosted models
Context Engineering and Prompt Design
- Structuring instructions for consistent and reliable behaviour
- Aggregating context from systems of record
- Utilising prompt caching and minimising token usage
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Implementing hybrid search, reranking, and citation generation
- Evaluating retrieval performance independently of generation
Agents and Tool Use
- Designing tools with typed interfaces
- Developing with OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure states
MCP and Enterprise Integration
- Understanding the Model Context Protocol 2026-07-28 revision
- Building and securing MCP servers
- Aligning tool permissions with enterprise identity systems
Evaluation as the Delivery Language
- Creating golden sets derived from real work
- Implementing automated scoring and LLM-based judges
- Establishing release gates linked to evaluation thresholds
Guardrails, Safety and Human Oversight
- Applying guardrails at prompt, tool, and policy levels
- Defining escalation paths and human review points
- Maintaining audit trails and complying with data handling duties
Deployment Architecture in the Customer Estate
- Navigating identity, network, and data residency constraints
- Using model gateways and multi-provider abstractions
- Implementing infrastructure as code and structured release processes
Observability, Cost and Token Economics
- Tracing agent executions and tool invocations
- Maintaining dashboards for quality and expenditure
- Reducing costs without compromising quality
Adoption and Change
- Enabling the workforce to utilise the new systems
- Cultivating champion networks and feedback mechanisms
- Measuring the rate of adoption
From Engagement to Reusable Capability
- Package proven workflows for reuse
- Create playbooks, blueprints, and accelerators
- Transition ownership to internal teams
Requirements
- Proficiency in Python, including experience with APIs and virtual environments
- Familiarity with cloud platforms, containerisation, and version control
- Prior experience calling LLM APIs or building prompt-based features
- Basic understanding of enterprise systems such as CRM, ERP, or ticketing platforms
Audience
- Forward deployed and applied AI engineers
- Software engineers transitioning into client-facing AI delivery
- Technical consultants and solutions architects responsible for AI rollouts
- Platform engineers supporting production AI workloads
